Key Points
1. Large language models (LLMs) have impressive abilities but struggle with factual errors and hallucinations due to outdated or incorrect knowledge.
2. Retrieval-augmented generation (RAG) is a useful method to address LLM issues, but its effectiveness relies heavily on the relevance and accuracy of retrieved documents.
3. Corrective Retrieval Augmented Generation (CRAG) is proposed to improve the robustness of generation by correcting the results of retrievers and enhancing the utilization of documents.
4. CRAG consists of a lightweight retrieval evaluator to assess the quality of retrieved documents, integration of large-scale web searches to augment retrieval results, and a decompose-then-recompose algorithm to selectively focus on key information in retrieved documents.
5. CRAG significantly improves the performance of RAG-based approaches across short- and long-form generation tasks, demonstrating its adaptability and generalizability.
6. Experiment results show that CRAG outperforms standard RAG and advanced RAG, demonstrating its effectiveness and flexibility in different generation tasks.
7. Ablation studies confirm the effectiveness of each triggered action and knowledge utilization operation in improving the robustness and utilization of knowledge.
8. The retrieval evaluator in CRAG significantly outperforms ChatGPT in accurately determining the overall quality of retrieval results.
9. CRAG's versatility, adaptability, and generalizability across diverse scenarios highlight its robust capabilities and effectiveness in addressing LLM issues.
Summary
CRAG for Improved Generation Robustness
The paper addresses the challenges faced by large language models (LLMs) when retrieval results are inaccurate, leading to generation inaccuracies or "hallucinations." The authors propose Corrective Retrieval-Augmented Generation (CRAG) to improve generation robustness. This includes a lightweight retrieval evaluator to assess the quality of retrieved documents, integration of large-scale web searches, and a decompose-then-recompose algorithm. CRAG is adaptable to different generation tasks and outperforms traditional retrieval-augmented generation methods.
Three Contributions of the Paper
The paper's contributions are threefold: (1) it studies scenarios where retrieval returns inaccurate results and designs corrective strategies for retrieval-augmented generation (RAG) to improve robustness, (2) it proposes CRAG as a plug-and-play method to improve automatic self-correction and efficient utilization of retrieved documents, and (3) experimental results demonstrate CRAG's adaptability to RAG-based approaches and its generalizability across short- and long-form generation tasks.
The experimental results show that CRAG significantly enhances the performance of RAG and Self-RAG across different datasets containing short- and long-form generation tasks. CRAG's versatility across diverse scenarios and its adaptability to different generation tasks underscore its robust capabilities. Ablation tests demonstrate the effectiveness of each triggered action and knowledge utilization operation designed in the retrieval evaluator. Additionally, the retrieval evaluator outperforms a competitive language model (ChatGPT), highlighting its effectiveness in determining the quality of retrieval results.
In summary, the proposed CRAG method addresses retrieval inaccuracies in large language models, enhances the utilization of retrieved documents for generation, and demonstrates its adaptability and generalizability in various generation tasks.
Reference: https://arxiv.org/abs/2401.15884